Performance of IRS-Assisted MIMO THz System Using Compressed Sensing-Based Measurement Matrix

• Published in: IEEE access Vol. 12; pp. 144950 - 144964
• Main Authors: Sharma, Vaishali, Kumar, Deepak, Sharma, Sanjeev, Bhatia, Vimal, Krejcar, Ondrej, Brida, Peter
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Array signal processing; Beamforming; Biomedical measurement; Channel estimation; Complexity; Compressed sensing; Compressed sensing (CS); Error analysis; Fading channels; Indoor environments; intelligent-reflecting surface (IRS); IRS signal-matched (IRSSM); Millimeter wave communication; MIMO communication; multiple-input multiple-output (MIMO); Receivers; Reconfigurable intelligent surfaces; Sampling; Signal generation; sixth-generation (6G); Terahertz communications; Terahertz frequencies; THz band; Wave propagation; Waveforms; Wireless communications
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3469535

Terahertz (THz) communications is a new frontier for the sixth-generation wireless systems due to availability of large bandwidth that supports terabits per second data rates. However, THz signals experience significant attenuation over distance, restricting their applicability primarily to indoor environments with limited range. Additionally, THz systems demand high Nyquist sampling rate, which increases computational complexity at the receiver. To address these challenges, intelligent-reflecting surfaces (IRSs)-assisted multiple-input multiple-output (MIMO) is a possible candidate that controls the propagation direction of THz waves. However, excessive dimensions of IRS and MIMO results in an enlarged near-field according to Rayleigh distance for THz bands. To mitigate the system complexity and reduce sampling to the sub-Nyquist rate, a low-complexity compressed sensing with transmit beamforming based receiver design is proposed for an IRS-aided MIMO THz system. The proposed approach utilizes an IRS signal-matched (IRSSM) measurement matrix to measure the transmitted signal at sub-Nyquist rate by exploiting sparsity of the waveform and the THz channels at the receiver. Furthermore, a closed-form expression of the average symbol error rate (ASER) is derived over generalized Nakagami-m fading for the considered network. Moreover, obtaining an ideal channel state information (CSI) is challenging in practice; hence, an imperfect CSI from the base station (BS) to the user is also considered. Simulation results demonstrate that the proposed IRSSM measurement matrix outperforms the prevailing matrices for the IRS-assisted MIMO THz systems.